Intelligent Blood Group Detection from Fingerprints Using Machine Learning Models
Keywords:
Fingerprint, Classification, Deep learning, Feature extraction, Blood Group.Abstract
The detection of blood groups traditionally requires invasive methods, such as blood sample collection and laboratory analysis. This paper presents a novel, non- invasive approach for predicting blood groups by analyzing fingerprint images. Fingerprint patterns are known to be influenced by genetic factors, which also govern blood group types. In this study, a dataset of fingerprint images from individuals with known blood groups (A, B, AB, and O) was analyzed using DL algorithms. Key features were extracted from the fingerprint patterns, including ridge count, ridge density, and minutiae distribution. These features were used to train a classification model for predicting the blood group. The proposed method achieved a promising accuracy of blood group detection, demonstrating the potential for integrating biometric and genetic information in non-invasive diagnostic tools. By providing a rapid, affordable, and non-invasive method of identifying blood group types, this strategy has the potential to completely transform medical diagnostic processes.
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